Stephen 52 Yahoo Com Gmail Com Mail Com 2020 21 Txt File

features = {}

# 7. File extension hint if 'txt' in tokens: features['file_extension'] = 'txt' features['looks_like_filename'] = True else: features['looks_like_filename'] = False stephen 52 yahoo com gmail com mail com 2020 21 txt

# 2. Name detection (if first token looks like a name) if tokens and tokens[0].isalpha() and tokens[0][0].isupper(): features['has_name'] = True features['first_token_is_name'] = tokens[0] else: features['has_name'] = False features = {} # 7

It looks like you’re asking to build a from a raw string of mixed data: Email-related fragments email_domains = ['gmail'

# 4. Email-related fragments email_domains = ['gmail', 'yahoo', 'mail', 'outlook', 'hotmail'] found_domains = [d for d in email_domains if d in tokens] features['email_domains_mentioned'] = found_domains features['email_domain_count'] = len(found_domains)

"stephen 52 yahoo com gmail com mail com 2020 21 txt" A deep feature in machine learning or data processing typically means extracting meaningful, higher-level attributes from raw input — going beyond simple keyword extraction into inferred patterns, relationships, or embeddings.

# 5. Possible email construction (name + domain) if features['has_name'] and found_domains: possible_emails = [f"{features['first_token_is_name']}@{d}.com" for d in found_domains] features['possible_emails'] = possible_emails